Abstract
Entity Linking (EL) is an essential task for semantic text understanding and information extraction. Popular methods separately address the Mention Detection (MD) and Entity Disambiguation (ED) stages of EL, without leveraging their mutual dependency. We here propose the first neural end-to-end EL system that jointly discovers and links entities in a text document. The main idea is to consider all possible spans as potential mentions and learn contextual similarity scores over their entity candidates that are useful for both MD and ED decisions. Key components are context-aware mention embeddings, entity embeddings and a probabilistic mention - entity map, without demanding other engineered features. Empirically, we show that our end-to-end method significantly outperforms popular systems on the Gerbil platform when enough training data is available. Conversely, if testing datasets follow different annotation conventions compared to the training set (e.g. queries/ tweets vs news documents), our ED model coupled with a traditional NER system offers the best or second best EL accuracy.
Highlights
Introduction and MotivationTowards the goal of automatic text understanding, machine learning models are expected to accurately extract potentially ambiguous mentions of entities from a textual document and link them to a knowledge base (KB), e.g. Wikipedia or Freebase
We report micro and macro InKB F1 scores for both Entity Linking (EL) and Entity Disambiguation (ED)
Iv) ED base model + att + global Stanford NER: our ED Global model that runs on top of the detected mentions of the Stanford NER system Finkel et al (2005)
Summary
Introduction and MotivationTowards the goal of automatic text understanding, machine learning models are expected to accurately extract potentially ambiguous mentions of entities from a textual document and link them to a knowledge base (KB), e.g. Wikipedia or Freebase. This problem is an essential building block for various Natural Language Processing tasks, e.g. automatic KB construction, question-answering, text summarization, or relation extraction. 1) MD may split a larger span into two mentions of less informative entities: B. Obama’s wife gave a speech [...] Federer’s coach [...] 2) MD may split a larger span into two mentions of incorrect entities: Obama Castle was built in 1601 in Japan. Romeo and Juliet by Shakespeare [...] Natural killer cells are a type of lymphocyte Mary and Max, the 2009 movie [...] 3) MD may choose a shorter span, referring to an incorrect entity: The Apple is played again in cinemas. 4) MD may choose a longer span, referring to an incorrect entity: Babies Romeo and Juliet were born hours apart The New York Times is a popular newspaper. 4) MD may choose a longer span, referring to an incorrect entity: Babies Romeo and Juliet were born hours apart
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